ORIGINAL ARTICLE
An approach to the meso-scale epidemiological behavior of Plasmodiophora brassicae from cruciferous crops under tropical conditions
1
Universidad del Magdalena, Facultad de Ingeniería, Santa Marta, Colombia
2
Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, 111321, Bogotá, Colombia
3
Laboratorio de Agrocomputación y Análisis epidemiológico, Center of Excellence in Scientific Computing, Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, sede Bogotá, Colombia
4
Plant Growth Facility Lead, Faculty of Agricultural, Life, and Environmental Sciences, 5-17 Ag/Forestry Building University of Alberta, Canada
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2024-06-20
Acceptance date: 2024-08-21
Online publication date: 2025-07-08
Corresponding author
Joaquín Guillermo Ramírez-Gil
Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, 111321, Bogotá, Colombia
Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, 111321, Bogotá, Colombia
HIGHLIGHTS
- Plasmodiophora brassicae causes clubroot disease.
- Plant/host interactions condition the pathology.
- Gaps in epidemiology limit IDM strategies.
- Meso-scale risk model based on edaphoclimatic factors.
KEYWORDS
TOPICS
ABSTRACT
Plasmodiophora brassicae is an obligate parasite and a natural soil inhabitant that
causes clubroot, a disease with significant economic impact in plants of the Brassicaceae
family. This pathology is conditioned by plant/host interactions, edaphoclimatic variables,
and mechanisms of inoculum dispersal. However, the epidemiology of this pathogen is not
well understood, thereby limiting its incorporation into integrated disease management
strategies (IDM). The objective of this work was to adjust a mesoscale risk and prognostic
model of P. brassicae based on edaphoclimatic factors and potential dispersal mechanisms
in brassica-producing areas in Colombia. The presence and inoculum density of the pathogen
were determined by visual inspection of symptoms and quantification by qPCR of soil
samples in a total of 127 plots located in regions with the highest production of species
from the Brassicaceae family. In addition, an edaphoclimatic characterization was carried
out based on field data and secondary information by web scraping using freely available
databases. The forecast models were determined by fitting a Generalized Linear Model
(GLM) using the logit and inverse link functions for binomial and gamma distributions,
respectively. The meso- and macroscale spatial risk model was developed under point pattern
approaches (Kernel density model and ecological niche model (ENM). The different
epidemiological analysis approaches used suggest that P. brassicae presents a high risk in
areas with host presence and conducive edaphoclimatic characteristics, indicating the need
to carry out epidemiological surveillance, reduce the dispersion of infested soil, and implement
P. brassicae exclusion methods.
RESPONSIBLE EDITOR
Mohamed Khan
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (52)
1.
Allouche O., Tsoar A., Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43 (6): 1223–1232. DOI: https://doi.org/10.1111/j.1365....
2.
Barve N., Barve V., Jiménez-Valverde A., Lira-Noriega A., Maher SP., Peterson AT., Soberón J., Villalobos F. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222: 1810–1819. DOI: https://doi.org/10.1016/j.ecol....
3.
Bhering A.S., do Carmo M.G.F., Matos T.M., Lima E.S.A., Sabrihno N.M.B.A. 2017. Soil factors related to the severity of clubroot in Rio de Janeiro, Brazil. Plant Disease 101: 1345–53. DOI: https://doi.org/10.1094/PDIS-0....
4.
Bithell F. 1990. An application of density estimation to geographical epidemiology. Statistics in Medicine 9: 691–701. DOI: https://doi.org/10.1002/sim.47....
5.
Botero-Ramírez A., García C., Gossen B.D., Strelkov S.E., Todd C.D., Bonham-Smith P.C., Pérez-López E. 2019. Clubroot disease in Latin America: distribution and management strategies. Plant Pathology 68 (5): 827–833. DOI: https://doi.org/10.1111/ppa.13....
6.
Botero-Ramírez A., Padilla-Huertas F.L., García C. 2022a. Soil, climate, and management practices associated with the prevalence of clubroot in Colombia. Agronomía Colombiana 40 (2): 228–236. DOI: https://doi.org/10.15446/agron....
7.
Botero-Ramírez A., Padilla-Huertas F.L., Strelkov S.E., García-Domínguez C. 2022b. The occurrence of clubroot in Colombia and iIts relationship with climate and agronomic practices. Horticulturae 8 (8): 711. DOI: https://doi.org/10.3390/hortic....
8.
Cao T., Manolii V.P., Strelkov S.E., Hwang S.-F., Howard R.J. 2009. Virulence and spread of Plasmodiophora brassicae [clubroot] in Alberta, Canada. Canadian Journal of Plant Pathology 31 (3): 321–329. DOI: https://doi.org/10.1080/070606....
9.
Cobos M.E., Peterson A.T., Barve N., Osorio-Olvera l. 2019. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ 7: e6281. DOI: https://doi.org/10.7717/peerj.....
10.
Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Böhner J. 2020. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8: 1991–2007. DOI: https://doi.org/10.5194/gmd-8-....
11.
DANE. 2020. Boletín técnico Encuesta nacional agropecuaria (ENA) 2019. [Online][Available from: https://www.dane.gov.co/files/...] [Accessed 20 February 2020].
12.
Diederichsen E., Frauen M., Ludwig-Müller J. 2014. Clubroot disease management challenges from a German perspective. Canadian Journal of Plant Pathology 36 (sup1.): 85–98. DOI: https://doi.org/10.1080/070606....
13.
Dixon G.R. 2009a. Plasmodiophora brassicae in its Environment. Journal of Plant Growth Regulation 28 (3). DOI: https://doi.org/10.1007/s00344....
14.
Dixon G. R. 2009b. The Occurrence and Economic Impact of Plasmodiophora brassicae and Clubroot Disease. Journal of Plant Growth Regulation 28 (3): 194–202. DOI: https://doi.org/10.1007/s00344....
15.
Dobson A.J, Barnett A.G. 2018. An Introduction to Generalized Linear Models. 4th ed. Chapman and Hall/CRC. New York, USA. DOI: https://doi.org/10.1201/978131....
16.
Donald C., Porter I., Porter I. 2009. Integrated control of clubroot. Journal of Plant Growth Regulation 28 (3): 289. DOI: https://doi.org/10.1007/s00344....
17.
Elith J., Phillips S.J., Hastie T., Dudík M., Chee Y.E., Yates C.J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17 (1): 43–57. DOI: https://doi.org/10.1111/j.1472....
18.
Escobar L.E. 2020. Ecological niche modeling: an introduction for veterinarians and epidemiologists. Frontiers in Veterinary Science 7: 519059. DOI: https://doi.org/10.3389/fvets.....
19.
Fick SE., Hijmans R.J. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37: 4302–4315. DOI: https://doi.org/10.1002/joc.50....
20.
Garrett K.A., Madden L.V., Hughes G. Pfender W.F. 2004. New applications of statistical tools in plant pathology. Phytopathology 94: 999–1003. DOI: https://doi.org/10.1094/PHYTO.....
21.
Gossen B.D., Strelkov S.E., Manolii V.P., Rennie D.C., Cao T., Hwang S.F., Peng, G., McDonald M.R. 2015. Spread of Plasmodiophora brassicae on canola in Canada, 2003–2014: Old pathogen, new home. Canadian Journal of Plant Pathology 37 (4): 403–413. DOI: https://doi.org/10.1080/070606....
22.
Hengl T., Mendes de Jesus J., Heuvelink G.B.M., Ruiperez M., Kilibarda M., Blagotić A., Shangguan W., Wright M.N., Geng X., Bauer-Marschallinger B., Guevara M.A., Vargas R., MacMillan R.A., Batjes N.H., Leenaars J.G.B., Ribeiro E., Wheeler I., Mantel S., Kempen B., Bond-Lamberty B. 2017. SoilGrids250m: Global gridded soil information based on machine learning. Plos One 12 (2): e0169748. DOI: https://doi.org/10.1371/journa....
23.
Hill T.B., Daniels G.C., Feng J., Harding M.W. 2022. Hard to kill: inactivation of Plasmodiophora brassicae resting spores using chemical disinfectants. Plant Disease 106 (1): 190–196. DOI: https://doi.org/10.1094/PDIS-0....
24.
Hittorf M., Letsch-Praxmarer S., Windegger A., Bass D., Kirchmair M., Neuhauser S. 2020. Revised Taxonomy and Expanded Biodiversity of the Phytomyxea (Rhizaria, Endomyxa). Journal of Eukaryotic Microbiology 67 (6): 648–659. DOI: https://doi.org/10.1111/jeu.12....
25.
Hwang S.F., Ahmed H.U., Zhou Q., Rashid A., Strelkov S.E., Gossen B.D., Peng G., Turnbull G.D. 2013. Effect of susceptible and resistant canola plants on Plasmodiophora brassicae resting spore populations in the soil. Plant Pathology 62 (2): 404–412. DOI: https://doi.org/10.1111/j.1365....
26.
Javed M.A., Schwelm A., Zamani-Noor N., Salih R., Silvestre Vañó M., Wu, J., González M., Marten T., Luo C., Prakash P., Pérez- López E. 2023. The clubroot pathogen Plasmodiophora brassicae: A profile update. Molecular Plant Pathology 24 (2): 89–106. DOI: https://doi.org/10.1111/mpp.13....
27.
LeDell E., Poirier S. 2020. H2O AutoML: Scalable automatic machine learning. p. 1–16. In: 7th ICML Workshop on Automated Machine Learning (2020). July 2020, USA.
28.
McCullagh P. 2019. Generalized Linear Models. 2nd ed. Routledge, New York, USA. DOI: https://doi.org/10.1201/978020....
29.
Madden L.V., Hughes G., van den Bosch F. 2007. The study of plant disease epidemics. p 421. St. Paul USA: American Phytopathological Society (APS Press). DOI: https://doi.org/10.1094/978089....
30.
Merow C., Smith M.J., Silander J.A. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36: 001–012. DOI: https://doi.org/10.1111/j.1600....
31.
Ministerio de Agricultura y Desarrollo Rural. 2023. Agronet: Área, producción y rendimiento nacional por cultivo. [Online] [Available from: https://www.agronet.gov.co/est...] [Accessed 26 February 2023].
32.
Molnar C. 2022. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed. p. 328. [Available on: christophm.github.io/interpretable-ml-book/] [Accessed on 9 October 2023].
33.
NASA Shuttle Radar Topography Mission. 2013. 30 m Digital Elevation Model. [Online] [Available on: www.earthdata.nasa.gov] [Accessed: 9 October 2023].
34.
Nelder J.A., Wedderburn R.W.M. 1972. Generalized linear models. Journal of the Royal Statistical Society: Series A. 135: 370–384. DOI: https://doi.org/10.2307/234461....
35.
Nelson M.R., Orum T.V., Jaime-Garcia R., Nadeem A. 1999. Applications of geographic information systems and geostatistics in plant disease epidemiology and management. Plant Disease 83: 308–319.
36.
Osorio‐Olvera L., Lira‐Noriega A., Soberón J., Peterson A.T., Falconi M., Contreras‐Díaz R.G., Barve N. 2020. ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods in Ecology and Evolution 11 (10): 1199–1206. DOI: https://doi.org/10.1111/2041-2....
37.
Phillips S., Anderson R., Schapire R. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 (3–4): 231–259. DOI: https://doi.org/10.1016/j.ecol....
38.
Ramírez-Gil J.G., Morales-Osorio J. G., Peterson A.T. 2021. The distribution of Phytophthora cinnamomi in the Americas is related to its main host (Persea americana), but with high potential for expansion. Phytopathologia Mediterranea, 60 (3): 521–534. DOI:https://doi.org/10.36253/phyto....
39.
Rennie D.C., Manolii V.P., Cao T., Hwang S.F., Howard R.J., Strelkov S.E. 2011. Direct evidence of surface infestation of seeds and tubers by Plasmodiophora brassicae and quantification of spore loads. Plant Pathology 60 (5): 811–819. DOI: https://doi.org/10.1111/j.1365....
40.
Saupe E.E., Barve V., Myers C.E., Soberón J., Barve N., Hensz C.M., Peterson A.T., Owens H.L., Lira-Noriega A. 2012. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. Ecological Modelling 237: 11–22. DOI: https://doi.org/10.1016/j.ecol....
41.
Shah D.A., De Wolf E.D., Paul P.A., Madden L.V. 2021. Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models. PLoS Computational Biology 17 (3): e1008831. DOI: 10.1371/journal.pcbi.1008831.
42.
Sharma K., Gossen B.D., McDonald M.R. 2011. Effect of Temperature on Cortical Infection by Plasmodiophora brassicae and Clubroot Severity. Phytopathology® 101 (12): 1424–1432. DOI: https://doi.org/10.1094/PHYTO-....
43.
Silverman B. 1998. Density Estimation for Statistics and Data Analyses. 1st ed. New York, USA.
44.
Skelsey P. 2021. Forecasting Risk of Crop Disease with Anomaly Detection Algorithms. Phytopathology 111: 321–332. DOI: https://doi.org/10.1094/PHYTO-....
45.
Strelkov S.E., Hwang S.-F. 2014. Clubroot in the Canadian canola crop: 10 years into the outbreak. Canadian Journal of Plant Pathology 36 (sup. 1.): 27–36. DOI: https://doi.org/10.1080/070606....
46.
Tewari J.P., Strelkov S.E., Orchard D., Hartman M., Lange R.M., Turkington T.K. 2005. Identification of clubroot of crucifers on canola (Brassica napus) in Alberta. Canadian Journal of Plant Pathology 27 (1): 143–144. DOI: https://doi.org/10.1080/070606....
47.
Timila R.D., Correll J.C., Duwadi, V.R. 2008. Severe and Widespread Clubroot Epidemics in Nepal. Plant Disease 92 (2): 317–317. DOI: https://doi.org/10.1094/PDIS-9....
48.
Title P.O., Bemmels J.B. 2018. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41 (2): 291–307. DOI: https://doi.org/10.1111/ecog.0....
49.
Torres E. 1972. Reacción de algunas crucíferas al ataque de Plasmodiophora brassicae Woronin en Manizales, Colombia. Acta Agronómica 22 (3–4): 185–207.
50.
Wallenhammar A.C. 1996. Prevalence of Plasmodiophora brassicae in a spring oilseed rape growing area in central Sweden and factors influencing soil infestation levels. Plant Pathology 45 (4): 710–719. DOI: https://doi.org/10.1046/j.1365....
51.
Vignali S., Barras A.G., Arlettaz R., Braunisch V. 2020. SDMtune: An R package to tune and evaluate species distribution models. Ecology and Evolution 10 (20): 11488–11506. DOI: https://doi.org/10.1002/ece3.6....
52.
Webster M.A., Dixon G.R. 1991. Calcium, pH and inoculum concentration influencing colonization by Plasmodiophora brassicae. Mycological Research 95 (1): 64-73. DOI: https://doi.org/10.1016/S0953-....
Share
RELATED ARTICLE
| eISSN: | 1899-007X |
| ISSN: | 1427-4345 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
